The invention relates to the combining of multiple images to produce combined images having superior quality.
In image processing systems, an image is often represented by a matrix of values in which each matrix value corresponds in some way to a position within a dimension (spatial, time, parameter, etc.) of the image that corresponds to the imaging subject. In two dimensional systems, the matrix element is known as a pixel; and in three-dimensional systems, the matrix element is known as a voxel (short for volume pixel). Any number of dimensions may be utilized in the imaging.
By way of a concrete example, in two dimensional imaging, it is common to represent images with pixels having 32-bit values. Such systems utilize four 8-bit channels, with each 8-bit channel able to accommodate a value between 0 and 255. The first three channels are associated with a color value, and may correspond with primary colors red, green and blue respectively. The last 8-bit channel, the alpha channel containing an alpha value, is really a mask and specifies how a pixel's colors should be merged with another pixel when two pixels are overlaid on top of one another. Nothing inherently limits the number of bits that can be allocated to a pixel.
In medical imaging, the process of image registration and fusion has become a common approach to align images from different modalities (or 2D and 3D images from the same or different modality) to bring additional information into a corresponding modality. A well-known approach is the fusion of positron emission tomography (PET) or single photon emission-computed tomography (SPECT) images (capable of showing tumor information, but little else) with computed tomography (CT) (capable of showing detailed anatomy).
Another approach, which has gained attention is the 2D3D registration, i.e., the registration of pre- or intra-operative 3D volume data to intraoperatively acquired fluoroscopic or angioscopic images. The known way to present these registered images is to blend them, i.e., to present an image where each pixel consists of a weighted sum of the corresponding pixels in each image (FIG. 1). In this known application, the same alpha value is used for the entire image.
One problem with this arrangement is that one of the images usually contains a lot of background pixels carrying no information. For example, in a 3D angioscopic image, only the vessels are of interest; in a PET image only the “hot spot” is of interest. By using the same value for each pixel (FIG. 1), these background pixels problematically reduce the contrast of the other image. This is an undesirable approach, especially for 2D3D registration where the 2D fluoroscopic image has little contrast to begin with.
According to the description below, the value of alpha ranges from zero to one that indicates a proportion of contribution of the image pixel for a particular image to an appertaining pixel in the combined image (the actual numbers used could be based on the number of bits available—e.g., for an 8-bit alpha channel, “0” (no contribution) could be represented by the integer 0, and “1” (full contribution) could be represented by the integer 255). For the images used in the combined images, the alpha values of the respective pixels should total one for a 100% total contribution. This is true regardless of the number of images to be combined, although for the sake of simplicity, only an embodiment with two images to be combined is described in detail.
Global Alpha Value
FIG. 1 illustrates the usual alpha blending in which one global (adjustable) alpha value is used for blending. Each pixel of Image 1 is weighted with a value α, as are all of the (e.g., white) background pixels containing no information. Each pixel of Image 2 is weighted with a value of (1−α), so in the blended image, those pixels of Image 2 containing information but corresponding to background pixels (the gray ones) are unnecessarily reduced in contrast.
Non-Threshold Based Alphas
It is known to provide an object-based use of alpha values for rendering overlapping objects in drawing programs—however, these objects user-created and are not determined from information obtained from the image itself. Similarly, the setting of alpha values of individual pixels is known, but no basis exists from setting these alpha values based on information obtained from the image.